





































Microsoft Word - ELP-V2N1-p90


Economics, Law and Policy 
ISSN 2576-2060 (Print) ISSN 2576-2052 (Online) 

Vol. 2, No. 1, 2019 
www.scholink.org/ojs/index.php/elp 

90 
 

Original Paper 

Affective Response and Attraction Effect on Consumer’s 

Intention to Buy 

Eric Santosa1* 

1 Economics & Business Faculty, Unisbank University, Semarang, Indonesia 
* Eric Santosa, Economics & Business Faculty, Unisbank University, Semarang, Indonesia 

 

Received: May 3, 2019        Accepted: May 17, 2019         Online Published: May 30, 2019 

doi:10.22158/elp.v2n1p90              URL: http://dx.doi.org/10.22158/elp.v2n1p90 

 

Abstract 

Studies of attraction effects commonly exercised by an experimental techniques, in which the effect is 

truly experienced. While the effect is apparently obvious, what is the consequence of generating an 

intention to buy? In addition, do the consumer’s moods and emotions affect the intention? If the moods 

are not fine does the consumer still want to choose the same brand/product? The answers are the 

purpose of the study. A sample which consists of 100 respondents is withdrawn by convenience and 

judgment method. Amos 16.0 and SPSS 16.0 are employed in analyzing data. The result shows that 

both, the attitude and subjective norm, are affected by the attraction effect. In addition, while the 

creation of attitude is affected by the attraction effect, it also influenced by the affective response. 

Futhermore, the customer’s intention to buy is built up as theorized. 

Keywords 

affective response, attraction effect, attitude, subjective norms, perceived behavioral control 

 

1. Introduction 

The attraction effect phenomenon declares that a particular object will be seemingly more appealing 

when another close objects’ attributes are inferior (Huber, Payne, & Puto, 1982; Huber & Puto, 1983; 

Ratneshwar, Shocker, & Stewart, 1987). In marketing the effect might lead to a tactical sales which let 

a particular product has higher transaction. Say, a Korean leather jacket which its price is $500 has no 

much attention when it is displayed alone in the corner of a perticular store. It will later on, be more 

attractive when the store owner pickes up other jackets which apparently their quality are not similar, 

look like inferior to the Korean jacket, while its prices are more expensive and they are placed around.  

Consumers likely prefer the product which is dominant to other/others. Its superiority obviously makes 

somebody to eagerly choose the product. Sentient Decision Science (2014) gives examples of two 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

91 
Published by SCHOLINK INC. 

high-end toasters. Toaster A has two slots which are wide enough for bagels, and costs $49. Toaster B 

has four slots which are wide enough for bagels as well, and costs $89. Which one will be choosen? By 

trading-off between number of slots and price, a customer might be willing to pony up the extra $40 

bucks and go fo Toaster B. When a third Toaster is added, it likely the choice changed. How come? It 

happens as follows. Toaster C has two slots, it costs $49, but it is not wide enough for bagels. Toward 

Toaster A, it has similar price, but based on the width it is inferior than A since it is not wide enough 

for bagels. It produces an attraction effect toward the Toaster A. While the Toaster A has a dominating 

position, it looks more appealing which inevitably increases the preference for Toaster A. 

Some studies also confirm the phenomenon, such as Kardes et al. (1989), Aaker (1991), Simonson and 

Tversky (1992), Lehman and Pan (1994), Sivakumar. and Cherian (1995), Lianxi et al. (1996), Doyle et 

al. (1999), Dhar and Simonson (2003), Kim and Hasher (2005), Kohler. (2007), Won (2012), Howes et 

al. (2016), and Gluth et al. (2017). Such occurrence also happens when the superiority does not only 

denote to both attributes, but also in a particular attribute only (asymmetrical dominance) (Simonson, 

1989; Simonson & Tversky, 1992; Huber & Puto, 1983; Hedgcock & Rao, 2009).  

Concerning with marketing, the attraction effect is basically not far from an individual’s desicion to 

choose a particular product. It is proclaimed that because of the effect, an individual might alter his/her 

choice from non-dominating product to dominating product. From psychological point of view, 

somebody might ask, what is the chronology of decision? What part of the process which finally 

activates the behavior (e.g., to choose the dominating product)? Santosa (2014, 2015) explores the 

influence of the effect on the activation of behavioral intention. While the activation of a particular 

behavior is preceded by a behavioral intention, the intention itself is ignited by an attitude, a subjective 

norm and a perceived behavioral control (Ajzen, 1991). Further, Santosa (2013, 2014, 2015) finds out 

that the process of generating the intention is inevitably affected by the attration effect, particularly the 

attitude and the subjective norm. In other word, the process of generating a behavior through an 

intention is obviously under the influence of the attraction effect.  

It is commonly understood that a behavior is resulted by affective and cognitive processes (Peter & 

Olson, 2002; Stangor, 2014). While Zajonc (1980) recognizes that feelings (affective) often precede 

cognitive processes, the thought is inevitably influenced by feelings. In addition, when cognitive is in 

process during making a decision, it is unavoidably affected by affective (Isen, 2001). While it is 

known that affect consists of positive and negative affect, some studies, such as Barone et al. (2000), 

Kahn and Isen (1993), Lee and Sternthal (1999) affirm that the positive affect enhances problem solving 

and decision making. A further study of Gable and Harmon-Jones (2010) state that positive and negative 

affects of low motivational intensity broaden attention, whereas positive and negative affects of high 

motivational intensity narrow attention. 

Since the attraction effect might alter a choice, and affect whether positive or negative, affected a 

thought, what kind of choice when the two simultaneously influence the cognitive processes? The 

answer is the purpose of this study that is to intensely know the influence of affective respond and 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

92 
Published by SCHOLINK INC. 

effect of attraction to customer’s behavior, particularly his/her behavioral intention. Some theoretically 

reviews are provided. An enlightenment of methods, analysis and findings are reported. 

Formulating Hypotheses 

a. The relation between the Attraction Effect (AE) with the Attitude (Ab) variable, and the Subjective 

Norm (SN) variable.  

In a cognitive system, the work of information and evaluation are in line; they work in the same 

direction. Information might lead to a thought, which in turn develops into a conviction (Peter 

& Olson, 2002). Whether information or evaluation makes a great contribution to assessing a 

particular object, it is inevitably affected by the assessor’s subjectivity. Thereby, an assessment 

towards a particular brand leads to a value, in which a consumer believes that the particular 

brand has a perceptive attribute in a particular product category (Pan & Lehmann, 1993). As a 

matter of fact, the perceptive attribute does not actually exist, it is an abstract. Therefore, each 

consumer might have a different perception (Schiffman & Kanuk, 2000).  

About the assessment itself, the consumer firstly classifies the information, incorporates it with 

their past experience, and later on comes to a conclusion which arises as a response (Peter & 

Olson, 2002). The subjective assessment occurs by means of a learning process related to the 

attribute’s dimensions, by comparing a brand with others, and even reducing the amount of the 

attribute’s dimensions which had previously just been perceived.  

With the great quantity of brands available and the attributes of each product category, this 

makes it very difficult for consumers to integrate and analyze information, so they simplify their 

decision making process through subjective judgments, or a belief in a particular brand. The reason 

is the limitations of people’s cognitive capacity (Bettman, 1979; Newell & Simon, 1972). In 

some studies on prices, consumers compared one price with others, resulting a perception of price. 

The price perception inevitably affected the consumers’ comprehension of the quality and 

value of the products, and hence the intention to buy (Dodds et al., 1991; Monroe & 

Petroshius, 1981). The becoming more interesting of a product when an inferior product comes 

closer (attraction effect) obviously demonstrates the subjective judgment of consumers, the 

subjective judgment will lead to an attitude creation through an integration of belief and evaluation.  

The subjective norm, which is developed through a normative belief and the motivation to comply, 

is apparently subjective. The more favorable aspects of the subjective norm clearly are in 

accordance with the inner wants, which always care for other people’s intentions. Therefore the 

subjective judgment of the attraction effect will also likely affect the subjective norm, when other 

people’s intentions arise from their subjective judgment of the attraction effect.  

These views apparently correspond to Santosa’s studies (2014, 2015) which show the influence of 

attraction effect on consumer’s attitude and subjective norm. Consequently, two hypotheses can be 

formulated as follows, 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

93 
Published by SCHOLINK INC. 

H1: The Attration Effect (AE) affects the Attitude’s creation (Ab). 

H2: The Attration Effect (AE) affects the Subjective Norm (SN). 

The affective system, as another point of view, automatically produces affective responses such ass 

emotions, specific feeling, moods and evaluation when stimuli come around (Peter & Olson, 2002). 

Since an attitude is one’s total evaluation to do something (Ajzen, 1991), it is assumed that the 

affective respons will unavoidably color an attitude. Some studies can be implemented, such as the 

finding of Mishra et al. (1993) which suggests the influence of motivation on attraction effect; 

Hedgcock and Rao (2009) proclaim that the introduction of a decoy into a trade-off-type choice set 

reduces “trade-off aversion”, or the decision maker’s experienced trade-off difficulty. A decoy is 

an option which causes preference reversals between the two other options in choice set (Herne, 

1997). A work of Kim and Hasher (2005) demonstrate that the efficacy of the attraction effect will 

be reduced in a particular condition.  

Some other studies are evidence for the effect of affect on decision making (Kahn & Isen, 1993; Lee 

& Sternthal, 1999; Barone et al., 2000; Isen, 2003). Isen and Erez (2002) indicate that positive affect 

interacts with task conditions in influencing motivation. Fredrickson and Branigan (2005) and Hicks 

and King (2007) assert that positive affect broadens attention. Harmon-Jones and Gable (2008) 

suggest that the intensity of approach motivation should be considered as this intensity plays a role in 

whether positive affect causes broadening or narrowing of attention. Fredrickson and Branigan (2005) 

and Gable and Harmon-Jones (2008) intensify their study and find out positive affects low in approach 

motivational intensity broaden attentional scope. Likewise, Gable and Harmon-Jones (2008) and 

Harmon-Jones and Gable (2009) emphasize positive affects high in approach motivational intensity 

narrow attentional scope. Gable and Harmon-Jones (2010) finally affirm that the effect of emotion on 

local/global precedence is not due to negative versus positive affect but is instead due to motivational 

intensity. Positive and negative affects of low motivational intensity broaden attention, whereas 

positive and negative affects of high motivational intensity narrow attention. The next hypothesis 

can be formulated as follows: 

H3: Affective Response (AR) affects the Attitude’s creation (Ab). 

b. The relation of Attitude toward behavior (Ab), the Subjective Norm (SN), and Perceived 

Behavioral Control (PBC) with Behavioral Intention (BI). 

While it is in accordance with the TRA and/or TPB that behavioral intentions can be predicted by 

attitude toward behavior, subjective norm and perceived behavioral control (Fishbein & Ajzen, 

1975; Ajzen, 1991), some studies (e.g., Jyh, 1998; Okun & Sloane, 2002; Martin & Kulinna, 

2004; Wiethoff, 2004; Marrone, 2005; Kouthouris & Spontis, 2005; Santosa, 2013; Santosa, 

2014; Santosa, 2015) are also in line with this theory. Thereby, the next hypotheses can be 

formulated as follows: 

 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

94 
Published by SCHOLINK INC. 

H4: The more favorable that the Attitude toward behavior (Ab) is, the greater the 

Behavioral Intention (BI) will be. 

H5: The more favorable the Subjective Norm (SN) is, the greater the Behavioral Intention 

(BI) will be. 

H6: The more favorable  Perceived Behavioral Control (PBC) is, the greater the 

Behavioral Intention (BI) will be. 

Research Model 

Based on the hypotheses a research model can be developed as follows in Figure 1. 

 

 

Figure 1. Research Model 

 

Identification : AE : Attraction Effect 

 AR : Affective Responds 

 Ab : Attitude toward behavior 

 SN : Subjective Norm 

 PBC : Perceived Behavioral Control 

 BI : Behavioral Intention 

 

2. Methods 

A sample is drawn using the convenience and judgment technique (Cooper & Schindler, 2001, 2008). 

Data are collected by questionnaires, distributed to respondents who have either already bought, or are 

interested in buying matic motorcycles. After examining the forms for the data’s completion, 100 out of 

the 106 questionnaire forms were accepted which supposed meet the sample adequacy (Ghozali, 2004, 

2007; Hair et al., 1995). A Likert scale is operated corresponding to a five-point scale ranging from 1 

(=completely disagree) to 5 (=completely agree). The instrument, which denotes to indicators, will firstly be 

justified through confirmatory factor analysis. Further, data are analyzed by employing Amos 16.0. 

 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

95 
Published by SCHOLINK INC. 

3. Result  

3.1 Confirmatory Factor Analysis 

First Phase CFA. The confirmatory factor analysis is not simultaneously carried out, but done in phases. 

The first phase contains two of independent variables, i.e., Attraction Effect (AE) and Attitude toward 

behavior (Ab). It actuslly also encloses two stages as well, firstly a relation which originally drawn 

from the variables’ character theirselves and secondly a relation which has already been repaired 

corresponding to good indices. Table 1 shows scores of indicators which relate to goodness of fit, and 

Figure 2, 3 and 4 depict the CFA itself. 

 

Table 1. First Phase, Second Phase, and Third Phase of CFA  

Indicators 1st Phase/2nd Stage 2nd Phase/2nd Stage 3rd Phase/2nd Stage Threshold  

Chi-square/Prob 666/0,717 226,220/0,000 434,905/0,000 29.588/p>0.05 

Cmin/df 0,333 13,307 24,161 ≤ 5 

GFI 0,997 0,800 0,749 High 

AGFI 0,965 0,577 0,498 ≥ 0,9 

TLI 1,007 0,616 0,390 ≥ 0,9 

RMSEA 0,000 0,333 0,457 0.05 s.d 0.08 

Source: data analysis. 

 

Second Phase CFA. It also contains two independent variables, i.e., Affective Responds (AR) and 

Subjective Norm (SN). It encloses two stages as well. While scores of indicators are represented at 

Table 1, the CFA itself is illustrated at Figure 3. 

 

,55

e1
,68

e2

chi-square= ,666
prob = ,717
cmin/df = ,333
GFI = ,997
AGFI = ,985
TLI = 1,007
RMSEA = ,000

,96

AE

2246,34

Ab

19,10

b ev

,04

1

,05

1

-,45

 

Figure 2. The CFA of AE and Ab 

 

 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

96 
Published by SCHOLINK INC. 

Third Phase CFA. It is similar with the previous two. It testifies the CFA between variable Perceived 

Behavioral Control (PBC) and Behavioral Intention (BI) which demonstrated whether at Table 1 or 

Figure 4. 

 

,63

e5
,65

e6

chi-square= 226,220
prob = ,000
cmin/df = 13,307
GFI = ,800
AGFI = ,577
TLI = ,616
RMSEA = ,333

1703,70

SN

NB MC

,04

1

,05

1

AR1

3,49
e1

1

AR2

1,16
e2

1

AR3

,61
e3

1

AR4

1,26
e4

1

5,54

AR

7,94

1,00 ,06,19 -,11

-,48 ,14

-,54

-1,02
-1,67

 

Figure 3. the CFA of AR and SN 

 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

97 
Published by SCHOLINK INC. 

,76

e5
,70

e6

chi-square= 434,905
prob = ,000
cmin/df = 24,161
GFI = ,749
AGFI = ,498
TLI = ,390
RMSEA = ,457

1520,48

PBC

PF CB

,05

1

,04

1

-,61

BI1

4,01
e1

1

BI2

,30
e2

1

BI3

,26
e3

1

BI4

,44
e4

1

6,48

BI

46,82

1,00 ,24,23 ,27

-,17
-,05 -,12

 

Figure 4. The CFA of PBC and BI 

 

Standardized Regression Weight of Indicators. The modification models of 1st, 2nd and 3rd phase CFA 

produce standardized regression weight for all indicators >0,4 which denote that the factor loading of 

the manifests are above the minimum requirement (Ferdinand, 2002) (Table 2). It indicates that all 

indicators of AR (AR1, AR2, AR3, AR4), Ab (b, ev), SN (NB, MC) and PBC (PF, CB), BI (BI1, BI2, 

BI3, BI4) are valid. 

3.2 The Structural Equation Model 

The model has three initial independents variable (AE, AR, PBC) and three dependent variables (Ab, 

SN, BI) in which the primary two dependent variables (Ab, SN) at some extent are treated as 

independent variables as well. Since the purpose of the study is eagerly to know the relationship 

between the two initial independents variable (AE, AR) and the primary dependent variables (Ab, SN), 

likewise among the three dependent variables separately and simultaneously, a structural equation 

modelling (sem) is employed (Hair et al., 1995). In addition, the use of SEM will give advantages such 

as fast, accurate and more detail. It is possible since the method performs a unification of factor 

analysis and path analysis (Ghozali, 2004, 2007). 

An initial structural equation model is drawn by connecting all variables as hypothesized. This model is 

likely not thoroughly appropriate to expectancy, since all indicators, i.e., Chi-Square/Prob, Cmin/df, 

GFI, AGFI, TLI, RMSEA, do not meet the criteria (Appendix A). Consequently, a modification model 

is generated by connecting e13 ↔ e14, e12 ↔ e14, e11 ↔ e14, e11↔ e13, e7 ↔ e8, e2↔ e8, e2↔ e4, 

e2↔ e3, e1↔ e4, and e9↔ e10. This modification model seemingly produces better scores than before 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

98 
Published by SCHOLINK INC. 

(Table 3, Figure 5). 

 

Table 2. Standardized Regression Weights 

   Estimate 

AR1 <--- AR 0.658 

AR2 <--- AR 0.557 

AR3 <--- AR 0.498 

AR4 <--- AR 0.556 

NB <--- SN 0.912 

MC <--- SN 0.920 

BI1 <--- BI 0.736 

BI2 <--- BI 0.712 

BI3 <--- BI 0.728 

BI4 <--- BI 0.641 

PF <--- PBC 0.908 

CB <--- PBC 0.893 

ev <--- Ab 0.935 

b <--- Ab 0.944 

Source: Amos output. 

 

Table 3. The Second Indicators Resulted from Modification  

Indicators Initial Scores Second Scores Threshold Justification  

Chi-square/Prob 922,427/0,000 334,423/0,000 31.264/p>0.05 Not meet the 

criterion 

Cmin/df 5,557 2,130 ≤ 5 Meet the criterion 

GFI 0,646 0,781 High Not meet the 

criterion 

AGFI 0,552 0,707 ≥ 0.9 Not meet the 

criterion 

TLI 0,685 0,922 ≥ 0.9 Meet the criterion 

RMSEA 0,203 0,101 0.05 s.d 0.08 Not meet the 

criterion 

Source: Data Analisis. 

 

 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

99 
Published by SCHOLINK INC. 

,55

e5

,68

e6

chi-square= 334,423
prob = ,000
cmin/df = 2,130
GFI = ,781
AGFI = ,707
TLI = ,922
RMSEA = ,101

,96

AE

Ab

b ev
1

-,45

5,54

AR

AR1 AR2 AR3 AR4

SN

NB MC

BI

BI1 BI2 BI3 BI4

,53

e1

,90

e2

,61

e3

,73

e4

,63

e7

,65

e8

,27

e11

,30

e12
,26

e13
,57

e14

,27 ,27,19 ,24

1111

,04 ,05

,24 ,24 ,23 ,27

1 1 1 1

19,56

11,67

1

1 1
1520,48

PBC

PF CB
,76

e9
,70

e10

,05 ,04

1 1

,05,04 ,02

,01

,02

4,48

1755,67

z1

1572,71

z2
3,65

z3

1

1

1

,07
12,12

19,98

-,16
-,25

-,15
-,08

-,54

-,38
-,48-,36

- 61  

Figure 5. Modified Model of the Initial Structural Equation Model 

 

Table 3 denotes that although not all the model’s indicators meet the criteria, some (Cmin/df and TLI) 

equalize the requirements. It means that the model’s data are in accordance with the structural parameter. 

As a consequent, the model is worthy of use. 

Evaluation of Normality. Evaluation of normality is carried out by univariate test (Ferdinand, 2002; 

Ghozali, 2004). It is exercised by scrutinizing the skewness value whether its critical ratio values are 

less or equal to ±2.58. As a matter of fact, there are seven variables, i.e., AE, AR, BI1, NB, AR4, AR3, 

and AR1, whose c.r of the skewness value are more than ±2.58. As a consequent, it indicates that 

univariately the data distribution is not normal. To check further, a multivariate test is executed. The 

result of the data analysis shows up that the multivariate critical value is 38,594. It is more than 2.58 as 

required (Appendix 5). As a result, the normality test needs a bootstrap analysis. 

Bootstrap Analysis. A bootstrap analysis is used to gain a fit model, since the normality test does not meet 

the pre-requisite. A Bollen-Stine’s bootstrap analysis illustrates the following: (a) The model fits better in 

498 bootstrap samples, (b) it fits equally well in 0 bootstrap samples, (c) it fit worse or failed to fit in 2 

bootstrap samples, (d) testing the null hypothesis that the model is correct, Bollen-Stine bootstrap p=0.006. 

While the result indicates that the probability is smaller than 0.05 which denotes that it can not reject 

the hull hypothesis, the model;s availability of use likely depends on the goodness of fit. As shown in 

appendix 3, the cmin/df=2.130 and TLI=0.922 suggest that the model is still worthy of use. 

 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

100 
Published by SCHOLINK INC. 

Outliers. Evaluation of the outliers can be carried out by either a univariate test or a multivariate test 

(Ferdinand, 2002). The univariate test is successfully employed by firstly converting the data to 

Z-scores, which should be less than ±3.0 (Hair et al., 1995). The result indicates that most of the 

variables’ Z-scores are less than ±3.0, except AE, AR, NB1, NB, and MC2, which their scores are more 

than ±3.0 (Appendix 3). Therefore, the existence of outliers is indicated. 

To check further, a multivariate outliers test is needed. It determines the chi-square value which 

subsequently is used as the upper limit, which could be calculated by searching on a chi-square table whose 

degree of freedom is equal to the number of variables employed, which is 37, under the degree of 

significance (p)=0.001. The chi-square value is found to be 69.292. In fact, most of the scores for 

Mahalanobis’s distance are less than 69.292, except observations number 1, which inevitably suggests 

outliers (Appendix 2). However, because there is no specific reason to dismiss them, the outliers are 

worth being used (Ferdinand, 2002). 

Multicollinearity and Singularity. According to the output from Amos, the determinant of the sample 

covariance matrix should be equal to 835,553. This value is far above zero. As a consequence, it 

belongs to no multicollinearity or singularity category (Appendix 4). 

Test of Hypotheses. The regression weights output indicates that the influence of AE on Ab and SN are 

significant. Likewise, the influence of AR on Ab. In addition, the influence of Ab on BI, SN on BI and 

PBC on BI are also significant (Table 4). 

 

Table 4. Regression Weights: Group Number 1-Default Model 

   Estimate S.E. C.R. P Label 

Ab <--- AE 19,558 4,058 4,820 *** par 12
SN <--- AE 11,672 3,839 3,041 ,002 par 13
Ab <--- AR 4,481 1,690 2,652 ,008 par 21
BI <--- Ab ,017 005 3,558 *** par 18
BI <--- PBC ,014 ,,005 2,584 ,010 par 19
BI <--- SN ,017 ,006 2,759 ,006 par 20

AR1 <--- AR ,269 ,029 9,207 *** par 2
AR2 <--- AR ,271 ,038 7,065 *** par 3
AR3 <--- AR ,191 ,032 6,054 *** par 4
AR4 <--- AR ,243 ,034 7,052 *** par 5
NB <--- SN ,043 ,002 23,480 *** par 6
MC <--- SN ,046 ,002 24,753 *** par 7
BI1 <--- BI ,240 ,019 12,490 *** par 8
BI2 <--- BI ,239 ,021 11,656 *** par 9
BI3 <--- BI ,234 ,019 12,224 *** par 10
BI4 <--- BI ,270 ,028 9,616 *** par 11
PF <--- PBC ,049 ,002 22,904 *** par 14
CB <--- PBC ,042 ,002 20,890 *** par 15
ev <--- Ab ,046 ,002 27,808 *** par 16
b <--- Ab ,045 ,001 30,129 *** par 17
Source: Amos output. 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

101 
Published by SCHOLINK INC. 

4. Discussion 

Table 4 shows that both the influence of AE on Ab and AE on SN are significant, which denoted by 

p=0.000 and p=0.002. These lead to the consequence that the hypotheses, i.e., “The Attration Effect (AE) 

affects the Attitude’s creation (Ab)”, and “The Attration Effect (AE) affects the Subjective Norm (SN)” 

are really empirically supported. This results are in accordance with the expectation which are in line 

with other Santosa’s studies findings (2013; 2014; 2015).  

The Table 4 also demonstrates that the influence of Affective Response (AR) to the attitude’s creation 

(Ab) is also empirically supported (H3). The finding is also in favor with other studies such as Mishra 

et al. (1993), Hedgcock and Rao (2009), Kim and Hasher (2005), Kahn and Isen (1993), Lee and 

Sternthal (1999), Barone et al. (2000), Isen (2003), Isen and Erez (2002), Fredrickson and Branigan (2005), 

Hicks and King (2007), Harmon-Jones and Gable (2008), Gable and Harmon-Jones (2008), Harmon-Jones 

and Gable (2009). However it is actually slightly different, since the finding denoted to the creation of an 

individual’s attitude concerning with the theory of planed behavior. Therefore, the attitude formed is not an 

attitude toward object, but an attitude toward behavior.  

The mentioned findings indicate that the attraction effect which simultaneously works with the 

affective response can develop a consumer’s subjective judgment, which through the integration of a 

consumer’s belief and evaluation can build up the consumer’s attitude. Meanwhile, the consumer’s 

subjective judgment leads to the consumers’ attitude, which is motivated by the need to comply with the 

desires of the people around him/her. Eventhough they do not look like totally new, it should be 

appreciated as a significant new facts in theoretical development, and obviously need further 

exploration and development.  

In accordance with the theory of planned behavior, the three predictors of behavioral intention, i.e., 

attitude, the subjective norm and perceived behavioral control work well. The results also support the 

studies of Jyh (1998), Okun and Sloane (2002), Martin and Kulinna (2004), Wiethoff (2004), Marrone 

(2005), Kouthouris and Spontis (2005), Santosa (2013), and Santosa (2015).  

The findings likely lead to managers to be very cautious of launching products. While the products 

should be carefully posted to generate an attraction effect, it is not easy to control consumers to be 

continuously happy since the consumers are vary. Many affairs are out of control. One way still open is 

to create, communicate and deliver excellent consumers’ value. It includes not only quality, but also 

feature, design, package, and price. The company should constantly develop brand and/or brand equity. 

In addition, the way of marketing the products should be well-performed, for instances, nice ads, 

showroom’s well-interior designed, interesting brochures, excellence support service, and salesforces’ 

well-performed. Any modes should lead to good first impression. Consequently, while the attraction 

effect is succesfully generated, the marketing efforts are obviously lead to good impression, the brand 

equity is well-developed it hopefully produces the intention to buy. 

 

 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

102 
Published by SCHOLINK INC. 

References 

Aaker, J. (1991). The Negative Attraction Effect? A Study of the Attraction Effect Under Judgment and 

Choice. In H. H. Rebecca, & R. S. Michael (Eds.), Advances in Consumer Research (pp. 462-469). 

Association for Consumer Research, Provo, UT. 

Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision 

Processes, 50, 179-211. https://doi.org/10.1016/0749-5978(91)90020-T 

Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Englewood 

Cliffs, NJ: Prentice Hall. 

Barone, M. J., Miniard, P. W., & Romeo, J. B. (2000). The influence of positive mood on brand extension 

evaluations. Journal of Consumer Research, 26, 387-402. https://doi.org/10.1086/209570 

Bettman, J. R. (1979). An Information Processing Theory of Consumer Choice. Reading, MA: 

Addison-Wesley. 

Cooper, D. R., & Pamela, S. S. (2001). Business Reserch Methods (7th ed). Boston: 

McGraw-Hill/Irwin. 

Cooper, D. R., & Pamela, S. S. (2008). Business Research Methods. Boston: McGraw-Hill/Irwin. 

Dhar, R., & Simonson, I. (2003). The Effect of Forced Choice on Choice. Journal of Marketing Research, 

40, 146-160. https://doi.org/10.1509/jmkr.40.2.146.19229 

Dodds, W. B., Kent, B. M., & Dhruv, G. (1991). Effect of Price, Brand, and Store Information on 

Buyer’s Product Evaluations. Journal of Consumer Re-search, 5(September), 138-142. 

Doyle, J., Connor, D. O., Reynolds, G., & Bottomley, P. (1999). The Robustness of the Asymmetrically 

Dominated Effect: Buying Frames, Phantom Alternatives, and In-Store Purchases. Psychology & 

Marketing, 16(3), 225-243. 

https://doi.org/10.1002/(SICI)1520-6793(199905)16:3<225::AID-MAR3>3.0.CO;2-X 

Ferdinand, A. (2002). Structural Equation Modeling Dalam Penelitian Manajemen. Semarang: BP 

Undip. 

Fishbein, M., & Azjen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory 

and Research. Reading, MA: Adisson-Wesley. 

Fredrickson, B. L., & Branigan, C. (2005). Positive emotions broaden the scope of attention and 

thought-action repertoires. Cognition & Emotion, 19(3), 13-332. 

https://doi.org/10.1080/02699930441000238 

Gable, P. A., & Harmon-Jones, E. (2008). Approach-motivated positive affect reduces breadth of 

attention. Psychological Science, 19, 476-482. https://doi.org/10.1111/j.1467-9280.2008.02112.x 

Gable, P. A., & Harmon-Jones. E. (2010). The Blues Broaden, but the Nasty Narrows: Attentional 

Consequences of Negative Affects Low and High in Motivational Intensity. Psychological Science, 

21(211). https://doi.org/10.1177/0956797609359622 

Ghozali, I. (2004). Model Persamaan Struktural: Konsep dan Aplikasi dengan Program Amos Ver 5.0. 

Semarang: BP Undip. 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

103 
Published by SCHOLINK INC. 

Ghozali, I. (2007). Aplikasi Analisis Multivariate dengan program SPSS. Semarang: BP Undip. 

Gluth, S., Jared, M. H., & Jörg, R. (2017). The Attraction Effect Modulates Reward Prediction Errors 

and Intertemporal Choices. The Journal of Neuroscience (2017). 

https://doi.org/10.1523/JNEUROSCI.2532-16.2016 

Hair et al. (1995). Multivariate Data Analysis. New Jersey: Prentice Hall. 

Harmon-Jones, E., & Gable, P. A. (2008). Incorporating motivational intensity and direction into the study 

of emotions: Implications for brain mechanisms of emotion and cognition-emotion interactions. 

Netherlands Journal of Psychology, 64, 132-142. https://doi.org/10.1007/BF03076416 

Harmon-Jones, E., & Gable, P. A. (2009). Neural activity underlying the effect of approach-motivated 

positive affect on narrowed attention. Psychological Science, 20, 406-409. 

https://doi.org/10.1111/j.1467-9280.2009.02302.x 

Hedgcock, W., & Rao, A. R. (2009). Trade-Off Aversion as an Explanation for the Attraction Effect: A 

Functional Magnetic Resonance Imaging Study. https://doi.org/10.1509/jmkr.46.1.1 

Herne, K. (1997). Decoy alternatives in policy choices: Asymmetric domination and compromise 

effects. European Journal of Political Economy, 13(3), 575-589. 

https://doi.org/10.1016/S0176-2680(97)00020-7 

Hicks, J. A., & King, L. A. (2007). Meaning in life and seeing the big picture: Positive affect and global 

focus. Cognition & Emotion, 21, 1577-1584. https://doi.org/10.1080/02699930701347304 

Howes, A., Warren, P. A., & Farmer, G. E. (2016). Why Contextual Preference Reversals Maximiza 

Expected Value. Psychological Review, 123(4), 368-391. https://doi.org/10.1037/a0039996 

Huber, J., & Puto, C. (1983). Market Boundaries and Product Choice: Illustrating Attraction and 

Substitution Effects. Journal of Consumer Research, 10(June), 31-44. 

https://doi.org/10.1086/208943 

Huber, J., Payne, J. W., & Puto, C. (1982). Adding Asymmetrically Dominated Alternatives: 

Violations of Regularity and Similarity Hypothesis. Journal of Consumer Research, 9(June), 

90-98. https://doi.org/10.1086/208899 

Isen, A. M. (2001). An Influence of Positive Affect on Decision Making in Complex Situations: 

Theoretical Issues with Practical Implications. Journal of Consumer Psychology, 11(2), 75-85. 

https://doi.org/10.1207/S15327663JCP1102_01 

Isen, A. M. (2003). Positive affect as a source of human strength. In A psychology of human strengths: 

Fundamental questions and future directions for a positive psychology (pp. 179-195). Washington, 

DC: American Psychological Association. https://doi.org/10.1037/10566-013 

Isen, A. M., & Erez, A. (2002). The influence of positive affect on the components of expectancy 

motivation. Journal of Applied Psychology, 87(6), 1055-1067. 

https://doi.org/10.1037/0021-9010.87.6.1055 

 

 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

104 
Published by SCHOLINK INC. 

Jyh-Shen, C. (1998). The Effect of Attitude, Subjective Norm, and Perceived Behavioral Control on 

Consumers Purchase Intentions: The Moderating Effects of Product Knowledge and Attention to 

Social Comparison Information. Proc.Natl. Sci. Counc. ROC (C), 9(2), 298-308. 

Kahn, B. E., & Isen, A. M. (1993). The influence of positive affect on variety-seeking among safe, enjoyable 

products. Journal of Consumer Research, 20, 257-270. https://doi.org/10.1086/209347 

Kardes, F. R., Paul, M. H., & Marlino, D. (1989). Some New Light on Substitution and Attraction 

Effects. Advances in Consumer Research, 16, 203-208. 

Kim, S., & Hasher, L. (2005). The Attraction Effect in Decision Making: Superior Performance by 

Older Adults. Q J Exp Psychol A, 58(1), 120-133. https://doi.org/10.1080/02724980443000160 

Kohler, W. R. (2007). Why does Context Matter? Attraction Effects and Binary Comparisons. In 

Working Paper Series No. 330. Zurich: Institute for Empirical Research in Economics, University 

of Zurich. https://doi.org/10.2139/ssrn.1001881 

Kouthouris, C. H., & Spontis, A. ( 2005). Outdoor Recreation Participation: An Application of the 

Theory of Planned Behavior. The Sport Journal, 8(3). 

Lee, A., & Sternthal, B. (1999). The effects of positive mood on memory. Journal of Consumer Research, 26, 

115-127. https://doi.org/10.1086/209554 

Lehmann, D. R., & Pan, Y. (1994). Context Effects, New Brand Entry, and Consideration Sets. Journal 

of Marketing Research, XXXI(August), 364-374. https://doi.org/10.1177/002224379403100304 

Lianxi, Z., Kim, C., & Laroche, M. (1996). Decision Processes of the Attraction Effect: A Theoretical 

Analysis and Some Preliminary Evidence. In P. C. Kim, & G. L. John (Eds.), NA-Advances in 

Consumer Research (Volume 23, pp. 218-224). Provo, UT: Association for Consumer Research.  

Marrone, S. R. (2005). Attitudes, Subjective Norms, and Perceived Behavioral Control: Critical Care 

Nurses’ Intentions to Provide Culturally Congruent Care to Arab Muslims. In Research Report 

(unpublished). Columbia University Teachers College. 

Martin, J. J., & Kulinna, P. H. (2004). Self-Efficacy Theory and Theory of Planned Behavior: 

Teaching Physically Active Physical Education Classes. Research Quarterly for Exercise and Sport, 

75(3), 288-297. https://doi.org/10.1080/02701367.2004.10609161 

Mishra, S., Umesh, U. N., & Stem, D. E. (1993). Antecedents of the attraction effect: An 

information-processing approach. Journal of Marketing Research, 30, 331-349. 

https://doi.org/10.1177/002224379303000305 

Monroe, K. B., & Petroshius, S. M. (1981). Buyer’s Perception of Price: An Update of the Evidence. In 

H. K. Harold, & S. R. Thomas (Eds.), Perspectives in Consumer Behavior (pp. 43-55). Glenview. 

IL: Scott, Foresman. 

Newell, A., & Simon, H. A. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice-Hall. 

Okun, M. A., & Sloane, E. S. (2002). Application of Planned Behavior Theory to Predicting Volunteer 

Enrollment by College Students in A Campus-Based Program. Social Behavior and Personality. 

Tempe: Arizona State University. https://doi.org/10.2224/sbp.2002.30.3.243 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

105 
Published by SCHOLINK INC. 

Pan, Y., & Lehmann, D. R. (1993). The Influence of New Brand Entry on Subjective Brand 

Judgements. Journal of Consumer Research, 20(June), 76-86. https://doi.org/10.1086/209334 

Peter, J. P., & Olson, J. C. (2002). Consumer Behavior and Marketing Strategy (6th ed.). New York: 

McGraw-Hill Book Company. 

Ratneshwar, S., Shocker, A. D., & Stewart, D. W. (1987). Toward Understanding the Attraction Effect: 

The Implication of Product Stimulus Meaningfulness and Familiarity. Journal of Consumer 

Research, 13(March), 520-533. https://doi.org/10.1086/209085 

Santosa, M. S. E. (2013). Understanding Customers’ Behavior to Choicing “Lembah Ngosit” 

Restaurant Using The Theory of Planned Behavior. Equilibrium, V(1), 40-55. 

Santosa, M. S. E. (2014). Pengaruh Efek Atraksi Terhadap Pengambilan Keputusan Konsumen 

Menurut Teori Perilaku Yang Terencanakan (Theory of Planned Behavior). Unisbank: Penelitian 

belum dipublikasikan. 

Santosa, M. S. E. (2015). Attraction Effect on Consumer’s Decision Making. International Journal of 

Applied Busines and Research (IJABER), 13(4), 1758-1780. 

Schiffman, L. G., & Kanuk, L. L. (2000). Consumer Behavior (7th ed.). London: Prentice-Hall 

International Ltd. 

Sentient Decision Science. (2014). The Attraction Effect: A Behavioral Science Principle that is 

Affecting Your Product’s Adoption Rate. Retrieved June 8, 2014, from http://www. 

sentientdecisionscience.com/attraction-effect-behavioral-science-principle-affecting-products-ado

ption-rate/ 

Simonson, I. (1989). Choice Based on Reasons: The Case of Attraction and Compromise Effects. 

Journal of Consumer Research, 7, 158-174. https://doi.org/10.1086/209205 

Simonson, I., & Tversky, A. (1992). Choice in Context: Tradeoff Contrast and Extremeness Aversion. 

Journal of Marketing Research, 29, 281-295. https://doi.org/10.1177/002224379202900301 

Sivakumar, K., & Cherian, J. (1995). Role of Product Entry and Exit on the Attraction Effect. Marketing 

Letters, 6(1), 45-51. https://doi.org/10.1007/BF00994039 

Stangor, C. (2014). Principles of Social Psychology—1st International Edition. New York, NY: Harper 

Collins College Publishers. 

Wiethoff, C. (2004). Motivation to Learn and Diversity Training: Application of the Theory of Planned 

Behavior. Human Resource Development Quarterly, 15(3). https://doi.org/10.1002/hrdq.1103 

Won, E. J. S. (2012). A Theoretical Investigation on the Attraction Effect Using the Elimination by 

Aspects Model Incorporating Higher Preference for Shared Features. Jourmal of Mathematical 

Psychology, 56(5), 386-391. https://doi.org/10.1016/j.jmp.2012.06.001 

Zajonc, R. B. (1980). Feeling and Thinking: Preferences Need No Inferences. American Psychologist, 35, 

151-175. https://doi.org/10.1037/0003-066X.35.2.151 

 

 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

106 
Published by SCHOLINK INC. 

Appendix 

 

Appendix 1. Initial SEM 

,55

e5

,68

e6

chi-square= 922,427
prob = ,000
cmin/df = 5,557
GFI = ,646
AGFI = ,552
TLI = ,685
RMSEA = ,203

,96

AE

Ab

b ev
1

-,45

5,54

AR

AR1 AR2 AR3 AR4

SN

NB MC

BI

BI1 BI2 BI3 BI4

,53

e1

,78

e2

,61

e3

,58

e4

,63

e7

,65

e8

,27

e11

,30

e12
,26

e13
,44

e14

,27 ,27,19 ,24

1111

,04 ,05

,24 ,24 ,23 ,27

1 1 1 1

19,56

11,67

1

1 1
1520,48

PBC

PF CB
,76

e9
,70

e10

,05 ,04

1 1

,05,04 ,02

,01

,02

4,48

1755,67

z1

1572,71

z2
3,65

z3

1

1

1

,07
12,12

19,98

 

 

Appendix 2. Observations Farthest from the Centroid (Mahalanobis Distance) (Group Number 1) 

Observation number Mahalanobis d-squared p1 p2 

1 111,000 ,000 ,000 

65 64,831 ,000 ,000 

21 63,752 ,000 ,000 

76 63,752 ,000 ,000 

28 60,727 ,000 ,000 

96 53,733 ,000 ,000 

3 46,118 ,001 ,000 

15 44,600 ,001 ,000 

4 43,170 ,002 ,000 

95 41,399 ,003 ,000 

107 39,629 ,006 ,000 

41 38,779 ,007 ,000 

12 36,648 ,013 ,000 

82 35,400 ,018 ,000 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

107 
Published by SCHOLINK INC. 

Observation number Mahalanobis d-squared p1 p2 

27 34,659 ,022 ,000 

60 32,429 ,039 ,000 

59 29,437 ,080 ,007 

31 27,110 ,132 ,222 

24 27,043 ,134 ,166 

35 26,821 ,140 ,152 

5 26,510 ,150 ,160 

103 26,395 ,153 ,128 

55 25,973 ,167 ,165 

70 25,631 ,178 ,190 

13 25,498 ,183 ,164 

56 25,295 ,190 ,157 

10 24,963 ,203 ,186 

88 24,853 ,207 ,158 

47 24,332 ,228 ,250 

53 24,249 ,232 ,211 

67 23,566 ,262 ,394 

32 23,161 ,281 ,491 

105 21,566 ,365 ,951 

33 21,522 ,367 ,934 

40 20,936 ,401 ,979 

29 20,559 ,424 ,990 

93 20,327 ,438 ,992 

38 20,240 ,443 ,990 

51 20,085 ,453 ,990 

72 19,363 ,498 ,999 

6 19,267 ,505 ,999 

104 19,132 ,513 ,999 

66 19,106 ,515 ,998 

77 19,018 ,521 ,998 

83 18,990 ,522 ,996 

2 18,987 ,523 ,993 

90 18,477 ,556 ,999 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

108 
Published by SCHOLINK INC. 

Observation number Mahalanobis d-squared p1 p2 

46 18,037 ,585 1,000 

50 17,916 ,593 1,000 

14 17,890 ,595 ,999 

58 17,686 ,608 1,000 

48 16,398 ,692 1,000 

57 16,394 ,692 1,000 

81 16,238 ,702 1,000 

74 15,921 ,722 1,000 

99 15,629 ,739 1,000 

37 15,625 ,740 1,000 

106 15,593 ,742 1,000 

75 15,498 ,747 1,000 

87 15,451 ,750 1,000 

17 15,401 ,753 1,000 

68 15,171 ,767 1,000 

94 14,995 ,777 1,000 

49 14,811 ,787 1,000 

80 14,799 ,788 1,000 

63 14,649 ,796 1,000 

102 14,599 ,799 1,000 

101 14,368 ,811 1,000 

45 14,331 ,813 1,000 

89 14,028 ,829 1,000 

36 14,018 ,830 1,000 

84 13,707 ,845 1,000 

11 13,634 ,849 1,000 

7 13,629 ,849 1,000 

25 13,416 ,859 1,000 

44 12,641 ,892 1,000 

69 12,641 ,892 1,000 

26 12,638 ,892 1,000 

100 12,066 ,914 1,000 

43 12,051 ,914 1,000 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

109 
Published by SCHOLINK INC. 

Observation number Mahalanobis d-squared p1 p2 

8 11,194 ,941 1,000 

92 11,109 ,943 1,000 

71 10,893 ,949 1,000 

97 10,508 ,958 1,000 

52 10,477 ,959 1,000 

19 10,402 ,960 1,000 

30 10,286 ,963 1,000 

20 10,218 ,964 1,000 

42 9,258 ,980 1,000 

16 8,965 ,983 1,000 

39 8,840 ,985 1,000 

54 8,599 ,987 1,000 

108 8,414 ,989 1,000 

79 8,200 ,990 1,000 

73 7,580 ,994 1,000 

98 7,333 ,995 1,000 

78 7,243 ,996 1,000 

109 6,999 ,997 1,000 

91 6,697 ,998 1,000 

61 6,577 ,998 1,000 

 

Appendix 3. Z-SCORE 

Descriptive Statistics 

 N Minimum Maximum Mean Std. Deviation 

Zscore(AE) 112 -3.10031 .96091 .0000000 1.00000000 

Zscore(AR1) 112 -2.62146 1.51380 .0000000 1.00000000 

Zscore(AR2) 112 -1.56694 2.08925 .0000000 1.00000000 

Zscore(AR3) 112 -2.83332 1.57406 .0000000 1.00000000 

Zscore(AR4) 112 -2.90498 1.27968 .0000000 1.00000000 

Zscore(AR) 112 -4.05821 2.70673 .0000000 1.00000000 

Zscore(b1) 112 -1.96997 1.38998 -2.1529163E-16 1.00000000 

Zscore(b2) 112 -2.71300 1.47812 -1.4487270E-16 1.00000000 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

110 
Published by SCHOLINK INC. 

Zscore(b3) 112 -2.18318 1.15113 .0000000 1.00000000 

Zscore(b) 112 -2.35666 1.63580 .0000000 1.00000000 

Zscore(ev1) 112 -2.68286 1.37769 .0000000 1.00000000 

Zscore(ev2) 112 -2.74946 1.45449 .0000000 1.00000000 

Zscore(ev3) 112 -2.21975 1.29834 .0000000 1.00000000 

Zscore(ev) 112 -2.20775 1.64912 .0000000 1.00000000 

Zscore(Ab) 112 -1.97744 1.99244 .0000000 1.00000000 

Zscore(NB1) 112 -3.22802 1.59249 -1.3896141E-15 1.00000000 

Zscore(NB2) 112 -2.70077 1.64114 -1.0294074E-15 1.00000000 

Zscore(NB3) 112 -2.75263 1.74802 .0000000 1.00000000 

Zscore(NB) 112 -3.58129 2.05364 -1.3717487E-16 1.00000000 

Zscore(MC1) 112 -1.91645 1.49057 .0000000 1.00000000 

Zscore(MC2) 112 -3.25126 1.37275 .0000000 1.00000000 

Zscore(MC3) 112 -1.48560 1.82010 .0000000 1.00000000 

Zscore(MC) 112 -2.35117 2.00541 -2.2601550E-16 1.00000000 

Zscore(SN) 112 -2.86260 2.49178 .0000000 1.00000000 

Zscore(PF1) 112 -3.72033 .90941 -1.1742841E-15 1.00000000 

Zscore(PF2) 112 -3.10558 1.50138 -7.5403932E-16 1.00000000 

Zscore(PF3) 112 -2.34377 1.90728 -4.0168166E-16 1.00000000 

Zscore(PF) 112 -3.88076 1.85713 .0000000 1.00000000 

Zscore(CB1) 112 -2.59820 1.06985 -3.1902962E-15 1.00000000 

Zscore(CB2) 112 -2.01890 1.62815 -8.3293832E-16 1.00000000 

Zscore(CB3) 112 -2.44287 1.84892 .0000000 1.00000000 

Zscore(CB) 112 -2.71984 2.11543 .0000000 1.00000000 

Zscore(PBC) 112 -2.51454 2.54053 -7.4558275E-17 1.00000000 

Zscore(BI1) 112 -3.41551 1.56869 -3.4337064E-16 1.00000000 

Zscore(BI2) 112 -2.07084 1.57210 .0000000 1.00000000 

Zscore(BI3) 112 -2.67441 1.11716 -2.5150896E-15 1.00000000 

Zscore(BI4) 112 -2.65744 1.51987 -9.9782980E-16 1.00000000 

Zscore(BI) 112 -2.76612 1.92790 -2.6997041E-15 1.00000000 

Valid N (listwise) 112     



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

111 
Published by SCHOLINK INC. 

Appendix 4. Sample Covariances (Group Number 1) 

 
PBC AE AR Ab SN BI CB PF ev BI4 BI3 BI2 BI1 MC NB 

AR

4 

AR

3 

AR

2 

AR

1 
b 

PBC 
1520,48

2                    

AE 12,117 ,961 

AR 19,983 ,066 5,544 

Ab 674,394 19,099 26,138 
2246,3

37                 

SN 832,073 11,222 7,937 
1187,7

77 

1703,6

97                

BI 46,822 ,675 ,386 67,829 60,451 6,477 

CB 64,514 ,684 ,293 36,250 45,921 2,746 
3,43

4              

PF 73,756 ,440 1,243 24,510 31,384 1,688 
2,52

0 

4,33

5             

ev 30,631 ,885 ,990 
102,96

0 
56,217 3,159 

1,56

2 

1,29

5 

5,39

7            

BI4 9,143 ,239 ,022 15,440 14,742 1,747 ,653 ,214 ,676 ,909 

BI3 12,309 ,244 ,207 17,492 12,001 1,518 ,707 ,424 ,786 ,294 ,620 

BI2 13,805 ,060 ,176 19,319 18,801 1,548 ,715 ,597 ,929 ,250 ,311 ,672 

BI1 12,225 ,130 ,030 16,073 15,705 1,554 ,704 ,485 ,798 ,311 ,289 ,325 ,638 

MC 33,740 ,490 ,184 52,735 78,107 2,733 
1,96

4 

1,09

7 

2,54

0 
,747 ,508 ,735 ,784 

4,23

0       

NB 38,183 ,447 ,389 53,224 73,185 2,581 
1,98

3 

1,65

3 

2,57

3 
,523 ,510 ,953 ,632 

2,81

1 

3,77

7      

AR4 11,395 ,173 1,348 13,407 4,930 ,373 ,434 ,517 ,427 ,086 ,178 ,104 ,049 ,138 ,284 ,906 

AR3 8,496 ,023 1,060 5,222 1,607 ,129 ,268 ,398 ,163 
-,00

8 
,121 ,106 

-,07

5 
,010 ,059 ,190 ,816 

   

AR2 -1,610 -,074 1,501 5,693 -,705 ,047 
-,19

6 
,033 ,320 

-,05

0 

-,00

3 
,014 ,060 

-,07

7 

-,02

0 
,142 

-,11

4 

1,18

6   

AR1 4,130 -,020 1,492 7,281 4,355 ,051 
-,06

9 
,366 ,329 ,030 ,018 ,024 

-,00

4 
,219 ,174 ,111 ,239 ,287 ,927 

 

b 29,927 ,814 1,224 
100,40

2 
52,607 3,031 

1,68

6 
,991 

4,14

9 
,705 ,812 ,869 ,670 

2,32

1 

2,36

0 
,739 ,223 ,196 ,279 

5,03

6 

Note. Condition number=143039,807. 

Eigenvalues 

3718,823 1136,082 642,633 6,541 4,399 1,559 1,256 

1,041 ,869 ,739 ,633 ,480 ,417 ,285 ,248 ,160 ,085 ,063 ,040 ,026 

Determinant of sample covariance matrix=835,553 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 2, No. 1, 2019 

112 
Published by SCHOLINK INC. 

Appendix 5. Assessment of Normality (Group Number 1) 

Variable min max skew c.r. kurtosis c.r. 

PBC 27,000 225,000 ,029 ,123 -,120 -,259 

AE 1,000 5,000 -1,073 -4,637 ,462 ,999 

AR 4,000 20,000 -,647 -2,794 1,616 3,492 

Ab 36,000 225,000 ,109 ,471 -,524 -1,133 

SN 3,000 225,000 ,240 1,039 ,625 1,351 

BI 8,000 20,000 -,363 -1,567 ,089 ,193 

CB 6,000 15,000 -,369 -1,595 ,215 ,465 

PF 3,000 15,000 -,736 -3,181 1,425 3,079 

ev 6,000 15,000 -,432 -1,867 -,328 -,708 

BI4 1,000 5,000 -,592 -2,556 -,307 -,664 

BI3 2,000 5,000 -,975 -4,213 1,039 2,244 

BI2 2,000 5,000 -,577 -2,492 -,095 -,205 

BI1 1,000 5,000 -,867 -3,746 ,910 1,967 

MC 6,000 15,000 -,165 -,712 -,094 -,203 

NB 4,000 15,000 -,615 -2,658 1,220 2,634 

AR4 1,000 5,000 -,910 -3,932 ,375 ,811 

AR3 1,000 5,000 -1,085 -4,688 ,186 ,402 

AR2 1,000 5,000 ,460 1,985 -,892 -1,928 

AR1 1,000 5,000 -,701 -3,030 -,360 -,778 

b 6,000 15,000 -,369 -1,595 -,229 -,495 

Multivariate 216,362 38,594 

 

 


